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Feng G, Gu Y, Wang C, Zhou Y, Huang S, Luo B. Wheat Fusarium Head Blight Automatic Non-Destructive Detection Based on Multi-Scale Imaging: A Technical Perspective. PLANTS (BASEL, SWITZERLAND) 2024; 13:1722. [PMID: 38999562 PMCID: PMC11243561 DOI: 10.3390/plants13131722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 06/17/2024] [Accepted: 06/19/2024] [Indexed: 07/14/2024]
Abstract
Fusarium head blight (FHB) is a major threat to global wheat production. Recent reviews of wheat FHB focused on pathology or comprehensive prevention and lacked a summary of advanced detection techniques. Unlike traditional detection and management methods, wheat FHB detection based on various imaging technologies has the obvious advantages of a high degree of automation and efficiency. With the rapid development of computer vision and deep learning technology, the number of related research has grown explosively in recent years. This review begins with an overview of wheat FHB epidemic mechanisms and changes in the characteristics of infected wheat. On this basis, the imaging scales are divided into microscopic, medium, submacroscopic, and macroscopic scales. Then, we outline the recent relevant articles, algorithms, and methodologies about wheat FHB from disease detection to qualitative analysis and summarize the potential difficulties in the practicalization of the corresponding technology. This paper could provide researchers with more targeted technical support and breakthrough directions. Additionally, this paper provides an overview of the ideal application mode of the FHB detection technologies based on multi-scale imaging and then examines the development trend of the all-scale detection system, which paved the way for the fusion of non-destructive detection technologies of wheat FHB based on multi-scale imaging.
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Affiliation(s)
- Guoqing Feng
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China; (G.F.); (Y.G.); (C.W.); (Y.Z.); (S.H.)
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- College of Agricultural Engineering, Jiangsu University, Zhenjiang 212000, China
| | - Ying Gu
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China; (G.F.); (Y.G.); (C.W.); (Y.Z.); (S.H.)
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Cheng Wang
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China; (G.F.); (Y.G.); (C.W.); (Y.Z.); (S.H.)
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- College of Agricultural Engineering, Jiangsu University, Zhenjiang 212000, China
| | - Yanan Zhou
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China; (G.F.); (Y.G.); (C.W.); (Y.Z.); (S.H.)
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Shuo Huang
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China; (G.F.); (Y.G.); (C.W.); (Y.Z.); (S.H.)
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Bin Luo
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China; (G.F.); (Y.G.); (C.W.); (Y.Z.); (S.H.)
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- College of Agricultural Engineering, Jiangsu University, Zhenjiang 212000, China
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